High resolution micrographs are often of poor quality, due to a variety of distortions and, especially, a very low signal-to-noise ratio. For micrographs of quasi-periodic arrays or sets of images of ostensibly identical free-standing particles, visual quality can be improved significantly by using correlation-averaging techniques. We have designed an iterative procedure that compensates for spatial deformation in quasi-periodic crystalline structures and allows noise reduction by averaging. This technique has been applied to the analysis of relaxed skeletal muscle filaments, as well as to filaments in a state of rigor. We have conducted an objective comparison of various normalization techniques and factorial representations. We found that the use of Principal Components Analysis together with a mean/variance normalization is the most favorable approach. We have improved the computational efficiency of the spectral signal-to - noise ratio (SSNR) resolution criterion by extending it for partial averages. In this approach, the initial data set is randomly partitioned into a given number of subsets, each subset is separately averaged, and a reduced form of the SSNR is computed over successive concentric annuli in Fourier space with increasing radial frequencies.